Imaging mass spectrometry has made significant advancements in recent years owing to its ability to study the spatial localization and abundance of proteins, peptides, lipids, glycans, metabolites, and drugs. In this study, we employed an imaging mass spectrometry-based workflow called MALDI-IHC, which uses antibodies conjugated with photocleavable mass-tags, to investigate the detection of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) along with pan-cytokeratin in formalin-fixed paraffin-embedded (FFPE) sections from cases with breast cancer. The expression of ER, PR, and HER2 was consistent with the clinical diagnosis based on conventional immunohistochemistry, lacking signal in triple-negative cases and concordant signals in the cases that were classified as ER+/PR+/HER2+, ER+/PR+/HER2-, and ER-/PR-/HER2+ groups, respectively. In addition, the potential utility of a seconday antibody-based MALDI-IHC approach for HER2 expression was evaluated. In agreement with immunohistochemistry results, HER2 expression co-localized within tumor regions in a HER2+ breast cancer case, while no expression was observed in a HER2- case. Collectively, these results highlight the potential of imaging mass spectrometry-based MALDI-IHC workflow for multiplexed detection of proteins in clinically relevant tissue sections.

Extracellular vesicles (EVs) are membrane-encapsulated nanoparticles with key roles in cell biology and growing importance in biomedicine. However, EVs are challenging to characterize because they are highly heterogeneous. Single EV analysis methods are valuable tools for addressing this challenge. Here, we developed single EV analysis pipeline for assessment of transmission electron microscopy (TEM) and single molecule localization microscopy (SMLM) images. Our analysis of morphology and localization/density patterns from these imaging techniques identified two primary EV classes which we termed dense and complex. Compared to dense EVs, complex EVs were larger, less circular, and more elongated. For recombinant and cell culture-derived EVs, analysis revealed overall good agreement between TEM and SMLM data. Using SMLM, we further compared plasma EVs enriched in common markers, tetraspanins CD9, CD63, and CD81, with the total EV population. While both groups showed similar size and shape properties, tetraspanin-enriched EVs were on average slightly larger and exhibited a higher fraction of complex EVs. Our findings underscore the morphological diversity of EVs; highlight the benefits of multimodal imaging; and demonstrate quantitative performance of the data analysis pipeline. Our comprehensive analysis of EV morphology may ultimately help clarify EV biogenesis and advance their potential in biomedicine. STATEMENT OF SIGNIFICANCE OF THE STUDY: Extracellular vesicles (EVs) have significant roles in biology and biomedicine. However, their inherent heterogeneity in size, shape, and molecular composition presents a major challenge for their characterization. To help address this challenge, we advanced a robust image analysis pipeline. We used this pipeline to analyze transmission electron microscopy and single molecule localization microscopy images, and we comprehensively quantified morphology of single EVs. By classifying EVs into distinct morphological subtypes, dense and complex, we reveal consistent trends across recombinant, cell line-derived, and plasma EVs. Importantly, we demonstrate strong agreement between imaging modalities. Our approach enables high-resolution analysis of individual EVs and offers insights into their structural diversity. This work advances the methodological toolkit for EV research and lays the foundation for improved characterization of EVs in biology and biomedicine.

Single-cell omics tools provide unique insights into heterogeneous cell populations and their responses to stimuli. For example, single-cell RNA sequencing has identified several transcriptionally distinct populations of microglia, which are resident immune cells of the central nervous system (CNS) that are responsive to CNS injury, infection, and neurodegeneration. To date, single-cell studies of microglia have focused on RNA-sequencing or cytometry by time of flight (CyTOF), which provide indirect readouts of protein abundance or quantification of a limited number of targets. Herein, we present a workflow based on FACS-assisted isolation, cryopreservation, and nanodroplet-based processing for single-cell mass spectrometry proteomics analysis of the postmortem human brain cortex-derived microglia. From a single microglial cell, 1039 proteins could be identified on average. As a proof-of-principle, we applied single-cell proteomics for exploring the heterogeneity of brain microglia at the cellular level. This pilot proteomics data partially recapitulates the prior microglia subtypes. Specifically, we determined that mitochondrial proteins, in particular members of NADH dehydrogenase (Complex I), cytochrome b-c1 (Complex III), cytochrome c oxidase (Complex IV), F1-ATPase (Complex V), and Na+/K+-ATPase complex, drive variation across microglia. This pipeline offers the potential for identifying functionally and analytically relevant protein targets for microglia in Alzheimer's disease and other neurological disorders. SIGNIFICANCE OF THE STUDY: Microglia are a key brain cell type that may contribute to pathogenesis in neurodegenerative disease. Transcriptomic profiling of microglia from the central nervous system of humans and animal models has identified several subtypes of microglia, and complementary proteomic profiling of microglia is likely to provide functionally and therapeutically relevant targets. Single-cell proteomics studies of human-derived microglia are lacking. This work describes a label-free, single-cell proteomics approach for microglia isolated by fluorescence-activated cell sorting from a human donor that yields comparable numbers of identifications in comparison to prior single-cell RNA sequencing studies of microglia. This approach holds promise for enabling large-scale proteomics-based subtyping of microglia and studying their roles in neurodegenerative diseases.

MS-based proteomics offers powerful opportunities for biomarker discovery; nevertheless, it is associated with technical challenges, including missing values and batch effects. Although imputation and batch-correction methods are well established in proteomics, their impact remains incompletely characterized in large-scale clinical proteomics datasets. Here, we examine the practical impact and interaction of three popular imputation methods (Gaussian, ½ LOD, KNN) in combination with three batch-effect correction approaches (ComBat, ComBat with disease covariate, MNN) on differential abundance analysis in a CE-MS urine peptidomics dataset of 1,050 samples across 13 batches from chronic kidney disease (CKD) patients and controls. Downstream effects were assessed based on peptide validation between discovery and validation sets. Imputation method choice had minimal impact on the final list of disease-associated peptides (DAPs), given the missingness structure and normalization strategy. In contrast, batch-effect correction largely affected the results: MNN and especially unadjusted ComBat removed a large proportion of DAPs ( 50% and >90%, respectively), whereas inclusion of disease status in the ComBat model largely preserved biological signal. This study highlights how popular preprocessing choices can affect biological signal, showing that imputation and batch-effect correction interact and jointly influence downstream results, underscoring the need for caution when applying batch-effect correction. STATEMENT OF SIGNIFICANCE OF THE STUDY: Finding reliable biomarkers in clinical proteomics requires addressing the technical noise that can hide true biological signals. In this work, we examine the practical impact and interaction of commonly used imputation and batch correction methods on the list of peptides that emerge as differentially abundant. Instead of relying on simulations or small datasets, we examine a large, real-world urine-peptidomics cohort of more than 1,000 samples screened for chronic kidney disease. The results demonstrate that, in datasets such as the one used here, different preprocessing strategies can lead to substantially different outcomes. Imputation and batch-effect correction were found to be interdependent, and batch effect removal can lead to loss of meaningful biological differences, highlighting the importance of applying such corrections with caution.

Fragilariopsis cylindrus is a key diatom in the Southern Ocean, where low iron and manganese availability constrain primary production and biogeochemical activity. The molecular mechanisms used by polar diatoms, including F. cylindrus, to cope with trace metal limitations remain largely unexplored. Here we present phenotypic characterizations and proteomic profiles of F. cylindrus grown under controlled iron (low, medium, high) and manganese (low, high) conditions that reflect those observed in the Southern Ocean. Using data-independent acquisition mass spectrometry, we measured over 8000 unique proteins capturing diverse metabolic responses, including those related to photosynthesis, elemental transport, and intracellular trafficking. We confirm consistent expression of canonical iron stress proteins (e.g., phytotransferrin) under low iron, and identify additional candidate biomarkers for iron and manganese stress that could be explored in future laboratory and field experiments. Our data also support the notion that one flavodoxin isoform in F. cylindrus is iron responsive and one is not, and show that PsaE, a protein associated with the iron-rich photosystem-I, is upregulated under low iron. Altogether, this dataset is among the most comprehensive proteomic characterizations of trace metal physiology in polar diatoms to date, providing a foundation for connecting molecular responses to trace metal availability and ocean biogeochemistry.

Cells are comprised of a broad spectrum of structures that compartmentalize biochemical and signaling mechanisms. These structures can be comprised of many biomolecules, but especially lipids, proteins, and nucleic acids. Techniques are limited to quantify or discover new subcellular structures. We explored whether a proteomics approach using chemical crosslinking followed by size-exclusion chromatography and mass spectrometry (SEC-MS) of whole cell lysates can address this challenge. Formaldehyde crosslinking was used to preserve the weak molecular interactions responsible for many protein and nucleic acid assemblies. In this study, we perform the first formaldehyde crosslinking-assisted SEC-MS in a bacterial system. We demonstrate that when expressed ectopically in E. coli, large structures of a known assembly protein, FUS, can be detected through SEC-MS. We then show that E. coli proteins are enriched in particles of large or medium size due to formaldehyde crosslinking, which is the first analysis by formaldehyde and SEC-MS for a bacterial system. Last, analysis identified previously characterized E. coli protein assemblies and condensates, as well as potentially novel associations of prokaryote metabolism with large subcellular bodies. We propose this unbiased method can be used to stimulate or supplement targeted methods for discovery of new cellular bodies in a wide range of cell types.

Cobalt is an essential micronutrient but becomes toxic at elevated concentrations, requiring microorganisms to balance acquisition and detoxification. Aeromonas hydrophila, an opportunistic aquatic pathogen, is often encountered in metal-contaminated aquatic environments; however, its adaptive responses to cobalt stress have not been systematically characterized. Here, we applied quantitative proteomics to characterize the global protein response of A. hydrophila under cobalt stress. A total of 2767 proteins were identified, of which 724 were differentially abundant. Enrichment analyses indicated that cobalt exposure was associated with alterations in energy metabolism, oxidative phosphorylation, and ribosome-related pathways. Gene set enrichment analysis suggested an overall upregulation of ribosome-associated functions, accompanied by down regulation of carbon metabolism and the tricarboxylic acid cycle. Protein-protein interaction network mapping identified 15 functional clusters, with core modules linked to oxidative phosphorylation, ABC transport, carbohydrate metabolism, and Fe-S cluster biogenesis. Ten hub proteins associated with respiratory and transport systems were identified based on network topology. Functional validation using seven deletion mutants indicated that genes encoding shikimate kinase, glutaminase, and arsenate reductase contribute to cobalt tolerance. Together, these findings provide a systems-level view of how A. hydrophila adapts to cobalt stress, reveal candidate factors mediating metal resistance, and suggest potential targets for antimicrobial development and bioremediation strategies.

Extracellular vesicles (EVs) are heterogeneous and play important roles in intercellular communication, contributing to physiological and pathological processes. Since few markers currently exist to differentiate subtypes of EVs, this study aimed to determine proteomic and lipidomic differences among four EV subpopulations. Large and small EVs (L-EVs and S-EVs) were isolated from human mast cells (HMC-1) and monocytes (THP-1) by differential ultracentrifugation and then further separated by density cushions into two different densities [low-density (LD) and high-density (HD)]. L-EVs were pelleted at 16,500 × g, and S-EVs were pelleted at 118,000 × g. LD EVs were collected at 1.079-1.146g/mL, while HD EVs were collected at 1.146-1.185g/mL. The morphology, size and yield of EVs were determined by TEM and western blot. The proteome and lipidome of the EV subpopulations were determined with mass spectrometry. A total of 5364 proteins were quantified, and L-EVs LD were enriched in mitochondrial proteins such as TIMM/TOMM and MICOS proteins, while L-EVs HD were enriched in cytoskeleton- and cytokinesis-associated proteins, such as KIF proteins. S-EVs LD were enriched in tetraspanins, ADAM10 and ESCRT machinery proteins, while S-EVs HD were enriched in proteins commonly viewed as contaminants, such as histones, complement factors and collagen. Proteins involved in membrane trafficking between the plasma membrane and organelles, such as adaptor protein complexes, the conserved oligomeric Golgi complex, the trafficking protein particle complex, sortin-nexins, TBC1 domain proteins and coatomer subunits, were expressed at similar levels across all EV subtypes. Furthermore, 107 lipids were quantified, and phosphatidylethanolamine (PE) was less abundant in L-EVs LD as compared to the other EV subtypes, while ceramides were enriched in L-EVs as compared to S-EVs.This study demonstrates that there is a core proteome and lipidome that is similar across all four EV subtypes, but importantly, it also shows that a portion of the proteome and lipidome differs in EV subpopulations separated based on size and density. We suggest that these could be important markers in future EV studies and that they may reflect a different biogenesis and EV function.